This article presents an interactive control approach allowing a human user to teleoperate a robotic manipulator located nearby. With this approach, the user keeps his/her hands free, as only head movements are exploited to control the robot. The controller maps the 6 Degrees of Freedom (DoF) user's head position and orientation into the 6 DoF robot endeffector position and orientation. The robot can reach a large workspace thanks to the combination of two features. Firstly, a virtual wand between the user's head and the robot end-effector converts user's head pantilt rotations into large displacements of the robot end-effector center perpendicularly to the wand axis (2 DoF). Secondly, for the remaining 4 DoF (robot end-effector center displacement along the wand axis and robot en-effector orientation), realtime deformation of the virtual wand is triggered when the user reaches uncomfortable configurations due to his/her head workspace limitations. Additionally, the user gets, through an Augmented Reality (AR) Headset, a non-delayed visual feedback of the current virtual wand geometry and location. The paper includes a description of the setup and the proposed controller, detailing how the robot position/orientation is coupled to the user's head position/orientation. A set of elementary experiments with a constant-geometry wand is first presented, showing workspace limitations for some DoF. Then the wand reconfiguration is introduced in the experiments, leading to full control of 6 DoF manipulation tasks throughout a large workspace.

The prevailing and most effective approach to teleoperate a robotic arm involves a direct position-to-position mapping, imposing robotic end-effector movements that mirrors those of the user, Fig. 1-top. However, due to this one-to-one mapping, the robot's motions are limited by the user's capability, particularly in translation. Drawing inspiration from head pointers utilized in the 1980s, originally designed to enable drawing with limited head motions for tetraplegic individuals, we proposed a “virtual wand” mapping which could be used by participants with reduced mobility. This mapping employs a virtual rigid linkage between the hand and the robot's endeffector, Fig. 1-bottom. With this approach, rotations produce amplified translations through a lever arm, creating a “rotation-to-position” coupling and expanding the translation workspace at the expense of a reduced rotation space. In this study, we compare the virtual wand approach to the one-to-one position mapping through the realization of 6-DoF reaching tasks. Results indicate that the two different mappings perform comparably well, are equally well-received by users, and exhibit similar motor control behaviors. Nevertheless, the virtual wand mapping is anticipated to outperform in tasks characterized by large translations and minimal effector rotations, whereas direct mapping is expected to demonstrate advantages in large rotations with minimal translations. These results pave the way for new interactions and interfaces, particularly in disability assistance utilizing residual body movements (instead of hands) as control input. Leveraging body parts with substantial rotations could enable the accomplishment of tasks previously deemed infeasible with standard direct coupling interfaces.

Exoskeleton technologies have numerous potential applications, ranging from improving human motor skills to aiding individuals in their daily activities. While exoskeletons are increasingly viewed, for example, as promising tools in industrial ergonomics, the effect of using them on human motor control, particularly on inter-joint coordination, remains relatively uncharted. This paper investigates the effects of generic low-amplitude force fields applied by an exoskeleton on motor strategies in asymptomatic users. The force fields mimic common perturbations encountered in exoskeletons, such as residual friction, over/under-tuned assistance, or structural elasticity. Fifty-five participants performed reaching tasks while connected to an arm exoskeleton, experiencing one of five tested force fields. Their movements before and after exposure to the exoskeleton force field were compared. The study focuses both on spatial and temporal changes in coordination using specific metrics. The results reveal that even brief exposure to a low- amplitude force field, or to uncompensated residual friction and dynamic forces, applied at the joint level can alter the interjoint coordination, while task performance remains unaffected. The tested force fields induced varying degrees of changes in joint contributions and synchronization. This study highlights the importance of monitoring coordination changes to fully understand the impact of exoskeletons on human motor control and thus enable safe and widespread adoption of those devices.

Pattern-recognition-based arm prostheses rely on recognizing muscle activation to trigger movements. The effectiveness of this approach depends not only on the performance of the machine learner but also on the user’s understanding of its recognition capabilities, allowing them to adapt and work around recognition failures. We investigate how different model training strategies to select gesture classes and record respective muscle contractions impact model accuracy and user comprehension. We report on a lab experiment where participants performed hand gestures to train a classifier under three conditions: (1) the system cues gesture classes randomly (control), (2) the user selects gesture classes (teacher-led), (3) the system queries gesture classes based on their separability (learner-led). After training, we compare the models’ accuracy and test participants’ predictive understanding of the prosthesis’ behavior. We found that teacher-led and learner-led strategies yield faster and greater performance increases, respectively. Combining two evaluation methods, we found that participants developed a more accurate mental model when the system queried the least separable gesture class (learner-led). Our results conclude that, in the context of machine learning-based myoelectric prosthesis control, guiding the user to focus on class separability during training can improve recognition performances and support users’ mental models about the system’s behavior. We discuss our results in light of several research fields : myoelectric prosthesis control, motor learning, human-robot interaction, and interactive machine teaching.

This work investigates how arm swing movements measured by Inertial Motion Unit (IMU) sensors can be used to identify and maintain the walking state in a self-balanced lower-limb exoskeleton for medical use. When an exoskeleton is in a dynamical state during gait, short patterns in IMU signals (e.g. a braking movement) can be hard to extract. Therefore, by relying on a threshold-based classifier constructed upon descriptive features of actively maintained arm swing movements, it is possible to build a gait termination detection method in which the transition between the walking and standstill states occurs whenever arm movements cease, and the corresponding patterns in the IMU signals disappear. Analysis of arm IMU signals were used to identify three amplitude and coordination-based features for the classification architecture. An online implementation of this novel detection interface for maintaining the walking state was validated with 11 unimpaired participants using the Atalante exoskeleton, leading to high accuracy with less than 2% of false negatives when the arms were swinging at a high amplitude, and less than 15% when they were swinging at a medium amplitude.

Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.

When the arm prosthesis worn by an amputated Human being is not adequately configured with respect to the end-effector task, body compensations are often observed. Namely, to compensate for a wrong joint positioning on the robotic distal side, a subject trying to reach a desired position/orientation of his/her hand mobilizes his/her proximal joints, thus exploiting the redundancy of the human+robot kinematic chain.In this paper, we explore the possibility of exploiting this well-known behavior to reverse the causality: if we observe the posture of an amputated subject wearing a prosthesis during a hand positioning task, to what extent can we infer the positioning error of the prosthesis?To answer this question, we make the assumption that the adequate, or natural posture for a given task is one that optimizes a postural score. The proposed approach then consists in i) measuring the joint posture of the subject fitted with the prosthesis; ii) search for an alternative posture that optimizes a postural score within the null space of the human+robot kinematic chain and iii) compute the position error for the robot joints between the initial and the optimized posture.An experimental evaluation is provided with non amputated subjects who emulate erratic positioning of their distal joints during hand positioning tasks. Results show that joint errors are estimated with a precision that seems compatible with the implementation of a real time control algorithm.

Beyond the ultimate goal of prosthetics, repairing all the capabilities of amputees, the development line of upper-limb prostheses control mainly relies on three aspects: the robustness, the intuitiveness and the reduction of mental fatigue. Many complex structures and algorithms are proposed but no one question a common open-loop nature, where the user is the one in charge of correcting errors. Yet, closing the control loop at the prosthetic level may help to improve the three main lines of research cited above. One major issue to build a closed-loop control is the definition of a reliable error signal; this paper proposes to use body compensations, naturally exhibited by prostheses users when the motion of their device is inaccurate, as such. The described control scheme measures these compensatory movements and makes the prosthesis move in order to bring back the user into an ergonomic posture. The function of the prosthesis is no longer to perform a given motion but rather to correct the posture of its user while s/he focus on performing an endpoint task. This concept was validated and compared to a standard open-loop scheme, for the control of a prosthetic wrist, with five healthy subjects completing a dedicated task with a customized transradial prosthesis. Results show that the presented closed-loop control allows for more intuitiveness and less mental burden without enhancing body compensation.

Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling.

Assist-as-needed control aims at maximizing stroke survivors involvement during robotic-led therapies of neurorehabilitation. Besides the specific characteristics of the designed adaptive control strategy, a fundamental property of this control architecture is the choice of the error signal which will drive the adaptation process. This driving source is a necessary control parameter to be chosen, although often sidelined in the control design, and several solutions already exist in the state-of-the-art. For this reason, we wanted to compare three different strategies to guide the adaptation, respectively based on the local joint performances, on the end-effector only behaviour, or on the performance of one specific joint in the kinematic chain of the robot. The resulting analysis evaluates the possibilities offered by simply changing from one source to another with respect to the specific stage of the motor recovery of the patients, potentially extending the capabilities of current exoskeleton controllers for neurorehabilitation.

Commercialized upper limb prostheses do not match the expectations of amputated people, especially transhumeral amputees. Most of them report a lack of functionality, mostly explained by a counter-intuitive control strategy. This paper presents the first implementation of an automatic prosthesis control approach based on natural coordinations between upper limb joints and IMU-based humeral orientation measurement. Two healthy individuals were able to use the prosthetic forearm attached to their upper arm to point at targets in a 3D workspace with a reasonable error. The results demonstrate the potential applications of automatizing the motion of some joints along the upper limb, in the same way as human upper limbs are controlled.

This article presents a versatile controller that enables various contact tooling tasks with minimal prior knowledge of the tooled surface. The controller is derived from results of neuroscience studies that investigated the neural mechanisms utilized by humans to control and learn complex interactions with the environment. We demonstrate here the versatility of this controller in simulations of cutting, drilling and surface exploration tasks, which would normally require different control paradigms. We also present results on the exploration of an unknown surface with a 7-DOF manipulator, where the robot builds a 3D surface map of the surface profile and texture while applying constant force during motion. Our controller provides a unified control framework encompassing behaviors expected from the different specialized control paradigms like position control, force control and impedance control.

This paper deals with the problem of computing trajectories for an exoskeleton that match a motion recorded on a given subject. Literature suggests that this problem can be solved by reconstructing the subject's joint motion using one of the numerous models available, and then feeding the exoskeleton with the joint trajectories. This is founded on the assumption that the exoskeleton kinematics reproduces the human kinematics. In practice, though, mismatches are unavoidable and lead to inaccuracies. We thus developed a method that is primarily based on an appropriate mechanical design: passive mechanisms are used to connect the exoskeleton with splints wore by the subject, in such a way that, within the workspace, there always exists a posture of the exoskeleton compatible with a given position and orientation of the splints. The trajectory computing method, by itself, consists of recording the position and orientation of the splints thanks to a conventional 3D motion tracker and to exploit standard robotics tools in order to compute an exoskeleton posture compatible with the measured human posture. Conclusive experimental results involving an existing 4 DoF upper-limb exoskeleton are shown.

When developing robotic exoskeletons, the design of physical connections between the device and the human limb to which it is connected is a crucial problem. Indeed, using an embedment at each connection point leads to uncontrollable forces at the interaction port, induced by hyperstaticity. In practice, these forces may be large because in general the human limb kinematics and the exoskeleton kinematics differ. To cope with hyperstaticity, the literature suggests the addition of passive mechanisms inside the mechanism loops. However, empirical solutions that are proposed so far lack proper analysis and generality. In this paper, we study the general problem of connecting two similar kinematic chains through multiple passive mechanisms. We derive a constructive method that allows the determination of all the possible distributions of freed degrees of freedom across different fixation mechanisms. It also provides formal proofs of global isostaticity. Practical usefulness is illustrated through two examples with conclusive experimental results: a preliminary study made on a manikin with an arm exoskeleton controlling the movement (passive mode) and a larger campaign on ten healthy subjects performing pointing tasks with a transparent robot (active mode).

Robotic exoskeletons can apply forces distributed on the limbs of the subject they are connected to. This offers a great potential in the field of neurorehabilitation, to address the impairment of interjoint coordination in hemiparetic stroke patients. In these patients, the normal flexible joint rotation synergies are replaced by pathological fixed patterns of rotation. In this paper, we investigate how the concept of synergy can be exploited in the control of an upper limb exoskeleton. The long term goal is to develop a device capable of changing the joint synchronization of a patient performing exercises during rehabilitation. The paper presents a controller able of generating joint viscous torques in such a way that constraints on joint velocities can be imposed to the subject without constraining the hand motion. On another hand, the same formalism is used to describe synergies observed on the arm joint motion of subjects realizing pointing tasks. This approach is experimented on a 4 Degrees Of Freedom (DoF) upper arm exoskeleton with subjects performing pointing 3-dimensional tasks. Results exhibit the basic properties of the controller and show its capacity to impose an arbitrary chosen synergy without affecting the hand motion.

The design of a robotic exoskeleton often focuses on replicating the kinematics of the human limb that it is connected to. However, human joint kinematics is so complex that in practice, the kinematics of artificial exoskeletons fails to reproduce it exactly. This discrepancy results in hyperstaticity. Namely, uncontrolled interaction forces appear. In this paper, we investigate the problem of connecting an exoskeleton to a human member while avoiding hyperstaticity; to do so, we propose to add passive mechanisms at each connection point. We thus introduce a formal methodology for avoiding hyperstaticity when connecting wearable robotic structures to the human body. First, analyzing the twist spaces generated by these fixation passive mechanisms, we provide necessary and sufficient conditions for a given global isostaticity condition to be respected. Then, we derive conditions on the number of Degrees of Freedom (DoFs) to be freed at the different fixations, under full kinematic rank assumption. We finally apply the general methodology to the particular case of a 4 DoF shoulder-elbow exoskeleton. Experimental results allow to show an improvement in transparency brought by the passive mechanism fixations.

A major issue in the field of human-robot interaction for assistance to manipulation is transparency. This basic feature qualifies the capacity for a robot to follow human movements without any human-perceptible resistive forces. In this paper we address the issue of human motion prediction in order to increase the transparency of a robotic manipulator. Our aim is not to predict the motion itself, but to study how this prediction can be used to improve the robot transparency. For this purpose, we have designed a setup for performing basic planar manipulation tasks involving movements that are demanded to the subject and thus easily predictable. Moreover, we have developed a general controller which takes a predicted trajectory (recorded from offline free motion experiments) as an input and feeds the robot motors with a weighted sum of three controllers: torque feedforward, variable stiffness control and force feedback control. Subjects were then asked to perform the same task but with or without the robot assistance (which was not visible to the subject), and with several sets of gains for the controller tuning. First results seems to indicate that when a predictive controller with open loop torque feedforward is used, in conjunction with force- feedback control, the interaction forces are minimized. Therefore, the transparency is increased.

We present a new device aimed at being used for upper limb rehabilitation. Our main focus was to design a robot capable of working in both the passive mode (i.e. the robot shall be strong enough to generate human-like movements while guiding the weak arm of a patient) and the active mode (i.e. the robot shall be able of following the arm without disturbing human natural motion). This greatly challenges the design, since the system shall be reversible and lightweight while providing human compatible strength, workspace and speed. The solution takes the form of an orthotic structure, which allows control of human arm redundancy contrarily to clinically available upper limb rehabilitation robots. It is equipped with an innovative transmission technology, which provides both high gear ratio and fine reversibility. In order to evaluate the device and its therapeutic efficacy, we compared several series of pointing movements in healthy subjects wearing and not wearing the orthotic device. In this way, we could assess any disturbing effect on normal movements. Results show that the main movement characteristics (direction, duration, bell shape profile) are preserved.